Architectures. We follow the same architectures for the generator and the UV space discriminator as described in ConvMesh [1]} for pre-training.
ConvMesh baseline uses a convolutional generator \(G\)  with two branches, to generate deformation map \(\mathbf {S} \in ^{32\times 32}\)  and texture map \(\mathbf {T} \in ^{512\times 512}\)  in the UV space respectively from a latent code \(\mathbf {z} \in ^{64}\) . The UV space discriminator consists of two sub-discriminators, for discriminating the deformation map and texture map respectively. In addition, we introduce an image space discriminator to further enforce the realism of the synthesized texture and shape, following the architecture of PatchGAN [2]}. We render the synthesized textured mesh to images with the DIB-R differentiable renderer [3]} following the Kaolin [4]} implementation.
